{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2c7f46b-af6e-4d26-9b30-625c94037afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 2500\n",
      "由模拟数据计算得到的汇总表：\n",
      "   区域   完成率  未完成占比\n",
      "0  华东  0.35   0.65\n",
      "1  西北  0.51   0.49\n",
      "2  东北  0.62   0.38\n",
      "3  华北  0.74   0.26\n",
      "4  华南  0.86   0.14\n",
      "\n",
      "目标表：\n",
      "   区域   完成率  未完成占比\n",
      "0  华东  0.35   0.65\n",
      "1  西北  0.51   0.49\n",
      "2  东北  0.62   0.38\n",
      "3  华北  0.74   0.26\n",
      "4  华南  0.86   0.14\n",
      "\n",
      "校验通过：汇总结果与目标表完全一致！\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===================== 0. 导入库与全局设置 =====================\n",
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "\n",
    "# 若是中文环境 Windows，一般有这些字体，可以避免中文方块\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']  # 任选其一存在即可\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "fake = Faker('zh_CN')\n",
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "Faker.seed(42)\n",
    "\n",
    "# ===================== 1. 用 Faker 生成模拟 erp_order 数据 =====================\n",
    "\n",
    "def generate_erp_orders(n_blocks_per_region: int = 5) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    生成模拟的 erp_order 数据。\n",
    "    逻辑：\n",
    "      - 按区域华东/西北/东北/华北/华南，每个区域以 100 单为一组，组内完成率严格等于目标百分比；\n",
    "      - 每个区域生成 n_blocks_per_region 组，因此总行数 = 5 * 100 * n_blocks_per_region。\n",
    "      - 完成订单：refund_status='未申请退款'\n",
    "      - 未完成订单：refund_status 随机为 '成功退款'/'部分退款'/'退款中'\n",
    "    \"\"\"\n",
    "\n",
    "    regions = ['华东', '西北', '东北', '华北', '华南']\n",
    "    target_rate = {\n",
    "        '华东': 0.35,\n",
    "        '西北': 0.51,\n",
    "        '东北': 0.62,\n",
    "        '华北': 0.74,\n",
    "        '华南': 0.86\n",
    "    }\n",
    "\n",
    "    start_dt = datetime(2022, 1, 1, 0, 0, 0)\n",
    "    end_dt   = datetime(2022, 6, 30, 23, 59, 59)\n",
    "\n",
    "    platforms = ['天猫旗舰店', '京东自营', '抖音小店', '线下门店']\n",
    "    stores = ['旗舰店A', '旗舰店B', '直播间C']\n",
    "\n",
    "    rows = []\n",
    "    auto_id = 1\n",
    "\n",
    "    for region in regions:\n",
    "        total_per_block = 100\n",
    "        complete_per_block = int(target_rate[region] * total_per_block)  # 35、51、62、74、86\n",
    "        incomplete_per_block = total_per_block - complete_per_block\n",
    "\n",
    "        for _ in range(n_blocks_per_region):\n",
    "            # 先生成“完成”的订单\n",
    "            for _ in range(complete_per_block):\n",
    "                order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "                payment_date = order_time + timedelta(minutes=random.randint(5, 6 * 60))\n",
    "                shipping_date = payment_date + timedelta(days=random.randint(0, 7))\n",
    "\n",
    "                unit_price = random.randint(80, 300)\n",
    "                quantity = random.randint(1, 3)\n",
    "                product_amount = round(unit_price * quantity, 2)\n",
    "                original_price = round(unit_price + random.randint(10, 100), 2)\n",
    "\n",
    "                row = {\n",
    "                    'id': auto_id,\n",
    "                    'internal_order_number': f'IN{fake.ean13()}',\n",
    "                    'online_order_number': f'ON{fake.ean13()}',\n",
    "                    'store_name': random.choice(stores),\n",
    "                    'full_channel_user_id': fake.uuid4().replace('-', ''),\n",
    "                    'shipping_date': shipping_date,\n",
    "                    'payment_date': payment_date,\n",
    "                    'payable_amount': product_amount,\n",
    "                    'paid_amount': product_amount,\n",
    "                    'status': '已完成',\n",
    "                    'consignee': fake.name(),\n",
    "                    'spu': f'SPU{random.randint(1000, 9999)}',\n",
    "                    'order_time': order_time,\n",
    "                    'province': region,  # 这里直接用大区名填到省份字段，便于后续汇总\n",
    "                    'city': fake.city_name(),\n",
    "                    'platform': random.choice(platforms),\n",
    "                    'sub_order_number': f'SUB{fake.ean8()}',\n",
    "                    'online_sub_order_number': f'OSUB{fake.ean8()}',\n",
    "                    'original_online_order_number': f'ORG{fake.ean13()}',\n",
    "                    'sku': f'SKU{random.randint(100000, 999999)}',\n",
    "                    'quantity': quantity,\n",
    "                    'unit_price': unit_price,\n",
    "                    'product_name': fake.word() + \" 商品\",\n",
    "                    'color_and_spec': random.choice(['红色 M', '黑色 L', '白色 S']),\n",
    "                    'product_amount': product_amount,\n",
    "                    'original_price': original_price,\n",
    "                    'is_gift': random.choice(['否', '是']),\n",
    "                    'sub_order_status': '已发货',\n",
    "                    'refund_status': '未申请退款',\n",
    "                    'registered_quantity': quantity,\n",
    "                    'actual_refund_quantity': 0\n",
    "                }\n",
    "                rows.append(row)\n",
    "                auto_id += 1\n",
    "\n",
    "            # 再生成“未完成”的订单\n",
    "            for _ in range(incomplete_per_block):\n",
    "                order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "                payment_date = order_time + timedelta(minutes=random.randint(5, 6 * 60))\n",
    "                # 未完成的单，有的已发货、有的未发货\n",
    "                shipping_date = payment_date + timedelta(days=random.randint(0, 7)) \\\n",
    "                                if random.random() < 0.7 else None\n",
    "\n",
    "                unit_price = random.randint(80, 300)\n",
    "                quantity = random.randint(1, 3)\n",
    "                product_amount = round(unit_price * quantity, 2)\n",
    "                original_price = round(unit_price + random.randint(10, 100), 2)\n",
    "\n",
    "                row = {\n",
    "                    'id': auto_id,\n",
    "                    'internal_order_number': f'IN{fake.ean13()}',\n",
    "                    'online_order_number': f'ON{fake.ean13()}',\n",
    "                    'store_name': random.choice(stores),\n",
    "                    'full_channel_user_id': fake.uuid4().replace('-', ''),\n",
    "                    'shipping_date': shipping_date,\n",
    "                    'payment_date': payment_date,\n",
    "                    'payable_amount': product_amount,\n",
    "                    'paid_amount': product_amount if random.random() < 0.8 else 0,\n",
    "                    'status': random.choice(['退款中', '已关闭']),\n",
    "                    'consignee': fake.name(),\n",
    "                    'spu': f'SPU{random.randint(1000, 9999)}',\n",
    "                    'order_time': order_time,\n",
    "                    'province': region,\n",
    "                    'city': fake.city_name(),\n",
    "                    'platform': random.choice(platforms),\n",
    "                    'sub_order_number': f'SUB{fake.ean8()}',\n",
    "                    'online_sub_order_number': f'OSUB{fake.ean8()}',\n",
    "                    'original_online_order_number': f'ORG{fake.ean13()}',\n",
    "                    'sku': f'SKU{random.randint(100000, 999999)}',\n",
    "                    'quantity': quantity,\n",
    "                    'unit_price': unit_price,\n",
    "                    'product_name': fake.word() + \" 商品\",\n",
    "                    'color_and_spec': random.choice(['红色 M', '黑色 L', '白色 S']),\n",
    "                    'product_amount': product_amount,\n",
    "                    'original_price': original_price,\n",
    "                    'is_gift': random.choice(['否', '是']),\n",
    "                    'sub_order_status': '售后中',\n",
    "                    'refund_status': random.choice(['成功退款', '部分退款', '退款中']),\n",
    "                    'registered_quantity': quantity,\n",
    "                    'actual_refund_quantity': random.randint(0, quantity)\n",
    "                }\n",
    "                rows.append(row)\n",
    "                auto_id += 1\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df\n",
    "\n",
    "\n",
    "# 生成数据：5 个区域 × 100 单/组 × 5 组 = 2500 条记录\n",
    "df_erp = generate_erp_orders()\n",
    "print(\"模拟订单数据条数：\", len(df_erp))\n",
    "df_erp.head()\n",
    "# ===================== 2. 计算各区域完成率，并与目标表校验 =====================\n",
    "\n",
    "# 按省份(这里就是大区)聚合，完成率=未申请退款 / 总订单数\n",
    "agg = (\n",
    "    df_erp\n",
    "    .assign(is_complete=lambda d: (d['refund_status'] == '未申请退款').astype(int))\n",
    "    .groupby('province', as_index=False)\n",
    "    .agg(\n",
    "        完成订单数=('is_complete', 'sum'),\n",
    "        总订单数=('id', 'count')\n",
    "    )\n",
    ")\n",
    "\n",
    "agg['完成率'] = (agg['完成订单数'] / agg['总订单数']).round(2)\n",
    "agg['未完成占比'] = (1 - agg['完成率']).round(2)\n",
    "\n",
    "# 按指定顺序重排：华东、西北、东北、华北、华南\n",
    "order = ['华东', '西北', '东北', '华北', '华南']\n",
    "agg = (\n",
    "    agg.set_index('province')\n",
    "       .loc[order]                # 按指定顺序重排\n",
    "       .reset_index()\n",
    ")\n",
    "\n",
    "# 把列名统一为“区域”，并确保是普通字符串类型\n",
    "agg = agg.rename(columns={'province': '区域'})\n",
    "agg['区域'] = agg['区域'].astype(str)\n",
    "\n",
    "summary_calc = agg[['区域', '完成率', '未完成占比']].reset_index(drop=True)\n",
    "\n",
    "# 目标表（与截图一致）\n",
    "summary_target = pd.DataFrame({\n",
    "    '区域': ['华东', '西北', '东北', '华北', '华南'],\n",
    "    '完成率': [0.35, 0.51, 0.62, 0.74, 0.86],\n",
    "    '未完成占比': [0.65, 0.49, 0.38, 0.26, 0.14],\n",
    "    '辅助': [1, 3, 5, 7, 9]\n",
    "})\n",
    "\n",
    "print(\"由模拟数据计算得到的汇总表：\")\n",
    "print(summary_calc)\n",
    "\n",
    "print(\"\\n目标表：\")\n",
    "print(summary_target[['区域', '完成率', '未完成占比']])\n",
    "\n",
    "# 校验：如果两表不一样会直接抛异常\n",
    "pd.testing.assert_frame_equal(\n",
    "    summary_calc[['区域', '完成率', '未完成占比']].reset_index(drop=True),\n",
    "    summary_target[['区域', '完成率', '未完成占比']].reset_index(drop=True)\n",
    ")\n",
    "print(\"\\n校验通过：汇总结果与目标表完全一致！\")\n",
    "\n",
    "# ===================== 3. 画图：复刻 Excel 深色进度条图（布局调整版） =====================\n",
    "\n",
    "# 为画图准备数据（降序：华南、华北、东北、西北、华东）\n",
    "plot_df = summary_target.copy()\n",
    "plot_df = plot_df.sort_values('完成率', ascending=False).reset_index(drop=True)\n",
    "\n",
    "regions = plot_df['区域'].tolist()\n",
    "rates = plot_df['完成率'].tolist()   # 0~1\n",
    "n = len(regions)\n",
    "y_pos = np.arange(n)\n",
    "\n",
    "# 画布改成更接近 PPT 的横向比例\n",
    "fig, ax = plt.subplots(figsize=(11, 6))\n",
    "\n",
    "# 整体背景色\n",
    "fig.patch.set_facecolor('#0f1633')\n",
    "ax.set_facecolor('#0f1633')\n",
    "\n",
    "# 坐标范围\n",
    "ax.set_xlim(0, 1.0)\n",
    "ax.set_ylim(-0.5, n - 0.5)\n",
    "\n",
    "# 灰色背景条（100%）\n",
    "bar_height = 0.6\n",
    "ax.barh(y_pos, [1.0] * n,\n",
    "        color='#70758a',       # 灰色\n",
    "        height=bar_height,\n",
    "        edgecolor='none')\n",
    "\n",
    "# 蓝色完成进度条\n",
    "ax.barh(y_pos, rates,\n",
    "        color='#008fe8',       # 亮蓝\n",
    "        height=bar_height,\n",
    "        edgecolor='none')\n",
    "\n",
    "# 在进度条末端画圆形节点 + 百分比文字\n",
    "for y, r in zip(y_pos, rates):\n",
    "    # 圆点\n",
    "    ax.scatter(r, y,\n",
    "               s=400,\n",
    "               facecolor='#008fe8',\n",
    "               edgecolor='white',\n",
    "               linewidth=2,\n",
    "               zorder=3)\n",
    "    # 百分比文字（86% 这种）\n",
    "    ax.text(r, y,\n",
    "            f\"{int(r * 100)}%\",\n",
    "            color='white',\n",
    "            fontsize=13,\n",
    "            ha='center',\n",
    "            va='center')\n",
    "\n",
    "# 左侧区域标签 —— 去掉之前的 transform，防止文字跑到最上面\n",
    "for y, region in zip(y_pos, regions):\n",
    "    ax.text(-0.03, y,                # x 取 0 左边一点点\n",
    "            region,\n",
    "            ha='right',\n",
    "            va='center',\n",
    "            fontsize=14,\n",
    "            color='white')\n",
    "\n",
    "# 去掉坐标轴和边框\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.tick_params(left=False, bottom=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# **关键：调整子图在画布中的位置，让条形图居中偏下，和 Excel 结构类似**\n",
    "plt.subplots_adjust(\n",
    "    left=0.18,   # 给左侧区域文字留空间\n",
    "    right=0.96,\n",
    "    top=0.75,    # 轴的顶部大约在 0.75，给标题预留上方空间\n",
    "    bottom=0.18  # 底部预留给脚注\n",
    ")\n",
    "\n",
    "# 主标题（靠上）\n",
    "fig.suptitle(\n",
    "    \"2022年上半年产品销量目标达成率情况\",\n",
    "    fontsize=26,\n",
    "    color='white',\n",
    "    fontweight='bold',\n",
    "    y=0.93            # 整个画布 0.93 的高度，更靠上\n",
    ")\n",
    "\n",
    "# 副标题（紧挨主标题下面）\n",
    "fig.text(\n",
    "    0.18, 0.83,\n",
    "    \"华南完成率最高达到86%，华东最低35%\",\n",
    "    fontsize=16,\n",
    "    color='white'\n",
    ")\n",
    "\n",
    "# 底部脚注\n",
    "fig.text(\n",
    "    0.05, 0.06,\n",
    "    \"*注：数据来源于公司销售系统，统计日期截至2022.06.30\",\n",
    "    fontsize=11,\n",
    "    color='white'\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "74fb4498-e6cf-42e1-86a1-57737cebc51f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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